The humans have the natural ability of following objects with the head and eyes and identify the relationship between those objects. This daily activity represents a challenge for computer vision systems. The procedure to identify the relationship between human eye gaze and the trackable objects is complex and demands several details. In this current paper we proposed a review of the main gazing following methods, identified the respective performance of them and also proposed an AI based method to estimate the gaze from 2D images based on head pose estimation. The main important details to be recovered from images are scene depth, head position and alignment and ocular rotation. In this approach we perform a track estimation of the gaze direction without the use of the eye position, and also, the face partial occlusion is considered in the analysis. The proposed approach allows low cost in processing with considerable accuracy at low complexity sceneries, because we don’t need to extract the facial features. Gaze tracking is important to evaluate employees’ attention to specific tasks in order to prevent accidents and improve work quality. The presented method improves the current knowing workflow by applying the head pose estimation instead face detection for training and inference. The promisors results are presented and open points are also discussed.